Overall, we find that 111 of our 780 structures (14.2%) are designable with an scTM score \(\ge 0.5\)  (Figure REF ), which is higher than the value of 11.8% reported by [1]}. Our improved designability comes from a significantly higher proportion of short structures (\(\le 70\)  residues) being designable (57/210 for ours compared to 36/210 in [1]}, \(p=0.014\) , Chi-square test). There is no significant difference in designability for longer structures of 71-128 residues (54/570 for ours compared to 51/570 in [1]}, \(p=0.76\) , Chi-square test). While ProteinSGM [4]} reports an even higher designability proportion of 50.5%, this metric is not directly comparable, as ProteinSGM generates sets of constraints that are then used to fold the final structure with Rosetta, rather than generating backbone structures directly as with our approach or that of [1]}. It is unsurprising that a structure produced by a dedicated protein folding tool produces greater designability. The authors themselves note that this Rosetta “post-processing” significantly improves the viability of their structures. To additionally contextualize our scTM scores, we benchmark against a naive baseline structure sampling method that preserves the overall distribution of protein bond angles but destroys their positional relationships; FoldingDiff significantly outperforms this baseline (see Appendix REF , Figure REF ).
